Job Description

Overview:

Overview:
Join the Prodapt team in building a unified, cloud-native environment for scalable machine learning training and experimentation. You will help design, develop, and optimize robust workflows that empower data scientists and engineers to efficiently explore, train, and validate ML models at scale.
Responsibilities:

Overall experience of 6+ years with proven track recordof over-achieving engineering, platform delivery and scaling targets in highvolume, innovativeand fast-paced high-pressure environment; proven results in delivery on platform products.

Solid proficiency in programming languages such as Python, Go and Java

Experience with cloud platforms (e.g., GCP, AWS, Azure) and containerization technologies (e.g., Docker, Kubernetes)

Experience with Vertex AI, Jupyter Notebook, Airflow, Kubeflow, Argo, GPU and HPC. Knowledge of GPU optimization and libraries is plus.

Experience building ML infrastructure or MLOps platforms and Bigdata platforms technologies such as Hadoop, BigQuery, Spark, Hive and HDFS.

Experience in machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models using various ML frameworks such as pytorch, tensorflow, scikit-learn, etc.

Experience in machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models using various ML frameworks such as pytorch, tensorflow, scikit-learn, etc.

Stayup-to-datewith the latest advancements in AI/ML technology and industrytrends andleverage this knowledge to enhance the platform's capabilities.

Strong communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner

Demonstrated leadership abilities, including the ability to inspire, mentor, and empower team members to achieve their full potential.

Requirements:

Expert in Java,ML,K8s and Devops.Develop, maintain, and enhance interactive Jupyter-based notebook environments for model development, experimentation, and training. o Build and optimize Pipelines (file-based and git-based) for orchestrating training and experiment jobs, ensuring seamless transition from research to production. o Integrate and manage Project Namespace storage for organizing research data and experiment artifacts. o Enable flexible compute resource allocation (CPU/GPU) for diverse ML workloads, including deep learning, NLP, and recommendation systems. o Implement and support experiment tracking tools for managing model versions, metadata, and reproducibility. o Develop and optimize data loading and processing workflows using technologies such as Spark, cuDF, RAPIDS, and NVTabular. o Leverage Google Cloud Platform (GCP) for scalable compute, storage, and orchestration of training jobs.